湘南柿竹园矿田云英岩型钨多金属成矿规律及成矿预测

湘南柿竹园矽卡岩型-云英岩型钨多金属矿田是中国最重要的钨多金属矿产资源基地之一。前人对该矿田的矽卡岩型成矿开展了系统的研究,而对矿田内云英岩型钨矿化研究薄弱,制约了矿田内成矿理论的认识和矿产勘查部署。通过野外调查,文章系统总结了矿田内云英岩型矿化样式、空间分布、矿化特征和控矿因素。研究显示柿竹园矿田内云英岩型矿体包含4种矿化样式：第一期斑状黑云母花岗岩中云英岩型矿体、石英斑岩中云英岩型矿体、第二期黑云母花岗岩中云英岩型矿体和矽卡岩-网脉状云英岩复合型矿体。这4种样式的云英岩型钨多金属矿体是柿竹园矿田内不同阶段的花岗岩成矿的产物。白钨矿化学成分显示矽卡岩型矿化的白钨矿低Mo,而云英岩型矿化白钨矿富Mo,指示云英岩矿化较矽卡岩矿化具有更氧化的环境。柿竹园矿田矿化格局显示云英岩型矿化受矿田和矿床尺度的花岗岩体侵位前锋控制,矿田尺度表现为岩体由北东深部向南西浅部侵位,千里山岩体南部为岩体侵位的前锋,岩体南部发育较大规模的云英岩矿体;矿床尺度上,云英岩体的定位受控于花岗岩岩突的控制。此外,矿田菱形格状构造对岩突产出位置具有重要的控制作用,也具有重要勘查指示意义。结合矿田控矿构造格局、不同期次岩浆岩对云英岩的控制及地球化学异常特征,笔者提出了大吉岭、柿竹园深部和柴山深部3处云英岩型钨多金属矿找矿预测靶区。     

Description of the biology of Caspian vimba, Vimba vimba (Linnaeus, 1758), in Gorgan Bay-Miankaleh Wildlife Refuge (southeast Caspian Sea)

We collected Vimba vimba throughout the spawning season (mid April to mid June, 2007) in Gorgan Bay (south-western Iran) and investigated its age, growth, and reproductive traits. The maximum age was 5⁺ years. Both sexes grew allometrically (positive for males: b=3.140 9 and negative for females: b=2.791 4). The von Bertalanffy growth functions were described by the formulae L _t =32.565(1-e^{−0.184(t+0.530)}) for males and L _t =35.950(1-e^{−0.179(t+0.529)}) for females. The overall sex ratio was balanced, but males were predominant in the smaller size classes and females in the larger size classes. Based on the gonadosomatic index (GSI) values, spawning appears to occur between late April and late May in the bay. The highest mean GSI was 6.44 for males in early May and 20.36 for females in late April. Absolute fecundity varies from the minimum of 5 436 eggs for age 3⁺ fish to the maximum of 36 141 eggs for age 5⁺ fish. Fecundity was also positively correlated with fish size (length and weight). Egg diameter ranged from 1.05 to 1.70 mm in the mean of 1.42 mm. There was no correlation between female size and ova diameter.     

Impact of rangeland enclosure and seasonal grazing on protected and unprotected rangelands in Chakwal region,Pakistan

Scrub rangelands support livestock grazing and provide ecosystem services to their inhabitants.The present study was conducted in Chakwal,an important tract of ...     

MULTI-ELEMENT ANALYSIS OF COASTAL MARINE SEDIMENT, AN IAEA PROPOSED REFERENCE MATERIAL (IAEA-356)

Proposed standard reference material, coastal marine sediment (IAEA-356) recently released under the IAEA Analytical Quality Control Services programme, has been analysed by instrumental neutron activation analysis technique. Up to 32 elements have been measured using the multi-element analysis approach. The precision of measurements varied from 1.3 to 12.5% with median value of 4.9%. The quality control of the data has been validated by analysing IAEA reference material of similar matrix which shows excellent agreement with IAEA values.     

Long-range precipitation forecasts using paleoclimate reconstructions in the western United States

Long-range precipitation forecasts are useful when managing water supplies.Oceanicatmospheric oscillations have been shown to influence precipitation.Due to a longer cycle of some of the oscillations,a short instrumental record is a limitation in using them for long-range precipitation forecasts.The influence of oscillations over precipitation is observable within paleoclimate reconstructions;however,there have been no attempts to utilize these reconstructions in precipitation forecasting.A data-driven model,KStar,is used for obtaining long-range precipitation forecasts by extending the period of record through the use of reconstructions of oscillations.KStar is a nearest neighbor algorithm with an entropy-based distance function.Oceanic-atmospheric oscillation reconstructions include the El Nino-Southern Oscillation(ENSO),the Pacific Decadal Oscillation(PDO),the North Atlantic Oscillation(NAO),and the Atlantic Multi-decadal Oscillation(AMO).Precipitation is forecasted for 20 climate divisions in the western United States.A 10-year moving average is applied to aid in the identification of oscillation phases.A lead time approach is used to simulate a one-year forecast,with a 10-fold cross-validation technique to test the models.Reconstructions are used from 1658-1899,while the observed record is used from 1900-2007.The model is evaluated using mean absolute error(MAE),root mean squared error(RMSE),RMSE-observations standard deviation ratio(RSR),Pearson’s correlation coefficient(R),NashSutcliffe coefficient of efficiency(NSE),and linear error in probability space(LEPS) skill score(SK).The role of individual and coupled oscillations is evaluated by dropping oscillations in the model.The results indicate ’good’ precipitation estimates using the KStar model.This modeling technique is expected to be useful for long-term water resources planning and management.     

Evaluation of various relationships of reaeration rate coefficient for modeling dissolved oxygen in a river with extreme flow variations in Pakistan

Mathematical relationships have been developed for reaeration rate coefficient (K_a) by various researchers. These relationships have a number of variables such as depth, velocity, width, slope, Froud number, molecular diffusion coefficient, kinematic viscosity and the gas‐transfer Reynolds number. From these variables, 29 relations have been developed and divided into four groups. To evaluate their predictive capability for highly variable flow rivers receiving high pollution loads form large cities, these relationships have been used to model dissolved oxygen (DO) in the River Ravi. Such rivers are either saturated with DO during high flows or anaerobic during critical low‐flow conditions. The evaluation is based on the agreement between model DO values calculated using K_a obtained from the available equations and the measured DO concentrations in the river samples in terms of sum of square of residuals (SSR) and coefficient of determination (R²). It has been found that in general, the group of equations containing depth and velocity as the only two variables affecting K_a performed better than the equations in other groups as reflected by lower SSR and higher R² values. The study results also reveal that the turbulence‐based reaeration rate coefficient equation containing additional variables also resulted in close agreement between DO model results and the measured values. The study results identify the most important parameters affecting the reaeration rate coefficient and the suitability of various K_a relationships as well for rivers with highly variable flows. Copyright © 2012 John Wiley & Sons, Ltd.     

Characterizing urban area dynamics in historic city of Kurukshetra,India, using remote sensing and spatial metric tools

Abstract

Developing countries like India are an urbanization hotspot with many upcoming towns and cities. Growth in small and medium sized towns and cities have been unnoticed and growing without appropriate urban planning. Utilizing the available medium resolution satellite data and geospatial platforms, the growth dynamics of Kurukshetra city was analysed over a period of 24 years. The study employed a combination of change detection technique and spatial metrics (six each of class and landscape levels) analysis to delineate the growth track of the city and its environs. A significant increase in urban built up (dense 237%; open 1038%) is seen majorly at the cost of open area (70%) and tree clad (58%). Phases of city’s aggregation and diffusion are observed using class and landscape level spatial metrics. Understanding and monitoring of land use changes in and around city limits using integrated spatial tools provide better decision making capability.     

Estimating soil moisture using remote sensing data: A machine learning approach

Soil moisture is an integral quantity in hydrology that represents the average conditions in a finite volume of soil. In this paper, a novel regression technique called Support Vector Machine (SVM) is presented and applied to soil moisture estimation using remote sensing data. SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach. SVM has been used to predict a quantity forward in time based on training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. SVM model is applied to 10 sites for soil moisture estimation in the Lower Colorado River Basin (LCRB) in the western United States. The sites comprise low to dense vegetation. Remote sensing data that includes backscatter and incidence angle from Tropical Rainfall Measuring Mission (TRMM), and Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) are used to estimate soil water content (SM). Simulated SM (%) time series for the study sites are available from the Variable Infiltration Capacity Three Layer (VIC) model for top 10 cm layer of soil for the years 1998–2005. SVM model is trained on 5 years of data, i.e. 1998–2002 and tested on 3 years of data, i.e. 2003–2005. Two models are developed to evaluate the strength of SVM modeling in estimating soil moisture. In model I, training and testing are done on six sites, this results in six separate SVM models – one for each site. Model II comprises of two subparts: (a) data from all six sites used in model I is combined and a single SVM model is developed and tested on same sites and (b) a single model is developed using data from six sites (same as model II-A) but this model is tested on four separate sites not used to train the model. Model I shows satisfactory results, and the SM estimates are in good agreement with the estimates from VIC model. The SM estimate correlation coefficients range from 0.34 to 0.77 with RMSE less than 2% at all the selected sites. A probabilistic absolute error between the VIC SM and modeled SM is computed for all models. For model I, the results indicate that 80% of the SM estimates have an absolute error of less than 5%, whereas for model II-A and II-B, 80% and 60% of the SM estimates have an error less than 10% and 15%, respectively. SVM model is also trained and tested for measured soil moisture in the LCRB. Results with RMSE, MAE and R of 2.01, 1.97, and 0.57, respectively show that the SVM model is able to capture the variability in measured soil moisture. Results from the SVM modeling are compared with the estimates obtained from feed forward-back propagation Artificial Neural Network model (ANN) and Multivariate Linear Regression model (MLR); and show that SVM model performs better for soil moisture estimation than ANN and MLR models.